A large number of bill recognition and processing apparatuses are used due to circulation of cashes around the world, such as money counting machines, cash sorters and ATMs in banking systems, vending machines in the retail industry and ticket venders in the intelligent transportation industry. A common feature of these apparatuses is that detection and recognition on bills are performed by recognition devices. A photosensitive sensor and a recognition algorithm are important for any recognition device.
Since recognition devices are applied to different application industries, the recognition devices are required to be adaptive to different requirements and application environments. It is required that a photosensitive sensor and a recognition algorithm have certain adaptive capabilities. For example, the sensor is required to be adaptive to changes in temperature and humidity to ensure stability and consistency of signal output. The recognition algorithm is required to be adaptive to bills of different wear levels, different denominations and different versions to ensure stability and consistency of recognition.
In existing products, regarding the photosensitive sensor, generally an output signal of the photosensitive sensor is corrected using a white reference film according to a photoelectric signal feedback compensation principle, and regarding the recognition algorithm, generally an appropriate threshold is determined by training with a large number of samples of real bills to be processed, and then the threshold is applied to the algorithm as a parameter to meet a specific product requirement.
In a process of collecting target images using a CIS, an image with inhomogeneous intensity may be outputted for a target with a homogeneous gray due to factors such as optical inhomogeneity, difference in responses of photosensitive cells, dark currents and bias, thereby adversely affecting target recognition and measurement in subsequent image processing. Therefore, before collecting target images using the CIS, it is required to calibrate the CIS in black and white. At present, among the known CIS inhomogeneity correction algorithms, a two-point method is effective in correcting the CIS non-homogeneity, which is under an assumption that each photosensitive unit responds linearly. A response line of the photosensitive cell can be obtained by only performing calibration measurement at two points of the line, thereby correcting non-homogeneity. However, recognition accuracy of the apparatus may be affected due to degradation in accuracy of photosensitive signal of the value document by variations of light-emitters and light-receiving components over time.
According to a feedback control principle in process control, a feedback system mainly includes a proportion section, an integration section and a differentiation section. In a traditional white reference-based photoelectric signal feedback correction method, only the proportion section is used to perform correction by multiplying a feedback signal deviation with a scale factor. With this method, a deviation of a sensor itself can be corrected in real-time to some extents, while an accumulation error of the entire system formed by the sensor and the recognition algorithm cannot be processed due to lack of the integration feedback section. In addition, the sensor is passive and cannot proactively predict a change of an object to be processed. Therefore, with the traditional method, a change of an object to be processed cannot be sensed and correction cannot be performed in advance due to lack of the differentiation feedback section.
Therefore, it is required to improve the design of the entire feedback control system and bring the integration and differentiation feedback control sections, so as to solve the problem of system accumulation error and perform a correction in advance.